Personalized syllabus generation using sub-concept sequences

ABSTRACT

One embodiment provides a method, including: receiving input identifying a goal of a student, wherein the goal indicates (i) a target concept to be learned by the student and (ii) a desired expertise corresponding to the target concept; receiving input indicating constraints comprising (i) a time budget and (ii) an effort budget; and generating a syllabus for the student to reach the goal, wherein the syllabus comprises a sequence of sub-concepts to be learned for reaching the goal, by: producing a plurality of alternative sequences of sub-concepts for reaching the identified goal, each sequence of sub-concepts having both a determined, corresponding (i) effort cost and (ii) time cost, wherein determining the corresponding effort cost and time cost comprises identifying relationships between sub-concepts; and determining, from the plurality of alternative sequences, a particular one of the sequences that reaches the target concept at the desired expertise and fulfills the indicated constraints.

BACKGROUND

To learn about a topic a person may take a course or access trainingmaterials directed at teaching the topic or a related topic that mayaddress the desired topic. For example, if a person wants to learn aboutalgebra, the person may take a college course that is directed towardsteaching algebra and topics necessary for learning algebra. As anotherexample, if a person wants to learn how to calculate the volume of afluid passing through a pipe, the person may take a fluid mechanicscourse. A person does not necessarily have to take a course through acollege or other education environment. Rather, the person may accessonline sources (e.g., online learning modules, online video tutorials,online resources, etc.), access company resources, buy books discussingthe topic, or the like. In other words, a person may learn about thedesired topic using different resources or a combination of resources.

BRIEF SUMMARY

In summary, one aspect of the invention provides a method, comprising:receiving input identifying a goal of a student, wherein the goalindicates (i) a target concept to be learned by the student and (ii) adesired expertise corresponding to the target concept; receiving inputindicating constraints of the student for reaching the identified goal,wherein the constraints comprise (i) a time budget corresponding to theamount of time the student is willing to expend and (ii) an effortbudget corresponding to a level of effort the student is willing toexpend; and generating a syllabus for the student to reach theidentified goal within the indicated constraints, wherein the syllabuscomprises a sequence of sub-concepts to be learned for reaching theidentified goal, wherein the syllabus is generated by: producing aplurality of alternative sequences of sub-concepts for reaching theidentified goal, each sequence of sub-concepts having both a determined,corresponding (i) effort cost and (ii) time cost, wherein determiningthe corresponding effort cost and time cost comprises identifyingrelationships between sub-concepts of one alternative sequence andsub-concepts of another alternative sequence; and determining, from theplurality of alternative sequences of sub-concepts, a particular one ofthe sequences of sub-concepts that both reaches the target concept atthe desired expertise and fulfills the indicated constraints.

Another aspect of the invention provides an apparatus, comprising: atleast one processor; and a computer readable storage medium havingcomputer readable program code embodied therewith and executable by theat least one processor, the computer readable program code comprising:computer readable program code configured to receive input identifying agoal of a student, wherein the goal indicates (i) a target concept to belearned by the student and (ii) a desired expertise corresponding to thetarget concept; computer readable program code configured to receiveinput indicating constraints of the student for reaching the identifiedgoal, wherein the constraints comprise (i) a time budget correspondingto the amount of time the student is willing to expend and (ii) aneffort budget corresponding to a level of effort the student is willingto expend; and computer readable program code configured to generate asyllabus for the student to reach the identified goal within theindicated constraints, wherein the syllabus comprises a sequence ofsub-concepts to be learned for reaching the identified goal, wherein thesyllabus is generated by: producing a plurality of alternative sequencesof sub-concepts for reaching the identified goal, each sequence ofsub-concepts having both a determined, corresponding (i) effort cost and(ii) time cost, wherein determining the corresponding effort cost andtime cost comprises identifying relationships between sub-concepts ofone alternative sequence and sub-concepts of another alternativesequence; and determining, from the plurality of alternative sequencesof sub-concepts, a particular one of the sequences of sub-concepts thatboth reaches the target concept at the desired expertise and fulfillsthe indicated constraints.

An additional aspect of the invention provides a computer programproduct, comprising: a computer readable storage medium having computerreadable program code embodied therewith, the computer readable programcode executable by a processor and comprising: computer readable programcode configured to receive input identifying a goal of a student,wherein the goal indicates (i) a target concept to be learned by thestudent and (ii) a desired expertise corresponding to the targetconcept; computer readable program code configured to receive inputindicating constraints of the student for reaching the identified goal,wherein the constraints comprise (i) a time budget corresponding to theamount of time the student is willing to expend and (ii) an effortbudget corresponding to a level of effort the student is willing toexpend; and computer readable program code configured to generate asyllabus for the student to reach the identified goal within theindicated constraints, wherein the syllabus comprises a sequence ofsub-concepts to be learned for reaching the identified goal, wherein thesyllabus is generated by: producing a plurality of alternative sequencesof sub-concepts for reaching the identified goal, each sequence ofsub-concepts having both a determined, corresponding (i) effort cost and(ii) time cost, wherein determining the corresponding effort cost andtime cost comprises identifying relationships between sub-concepts ofone alternative sequence and sub-concepts of another alternativesequence; and determining, from the plurality of alternative sequencesof sub-concepts, a particular one of the sequences of sub-concepts thatboth reaches the target concept at the desired expertise and fulfillsthe indicated constraints.

A further aspect of the invention provides a method, comprising:identifying a target topic to be learned; identifying alternativesequences of sub-topics within the target topic, wherein eachalternative sequence comprises a plurality of sub-topics that can belearned to learn the target topic and wherein each alternative sequencecomprises at least one sub-topic different from at least one anotheralternative sequence; learning relationships between sub-topics of onealternative sequence and sub-topics of another alternative sequence,wherein the learned relationships indicate an effort difference betweenone sub-topic of one alternative sequence and a sub-topic of anotheralternative sequence; and generating, based upon the learnedrelationships, a recommended course sequence that is within apredetermined effort level, wherein the recommended course sequencecomprises (i) one of the alternative sequences of sub-topics and (ii) alearning source for learning each of the sub-topics.

For a better understanding of exemplary embodiments of the invention,together with other and further features and advantages thereof,reference is made to the following description, taken in conjunctionwith the accompanying drawings, and the scope of the claimed embodimentsof the invention will be pointed out in the appended claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates a method of generating a syllabus for a student tolearn a target concept.

FIG. 2 illustrates an example identification of relationships betweensub-topics in alternative sequences.

FIG. 3 illustrates a computer system.

DETAILED DESCRIPTION

It will be readily understood that the components of the embodiments ofthe invention, as generally described and illustrated in the figuresherein, may be arranged and designed in a wide variety of differentconfigurations in addition to the described exemplary embodiments. Thus,the following more detailed description of the embodiments of theinvention, as represented in the figures, is not intended to limit thescope of the embodiments of the invention, as claimed, but is merelyrepresentative of exemplary embodiments of the invention.

Reference throughout this specification to “one embodiment” or “anembodiment” (or the like) means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the invention. Thus, appearances of thephrases “in one embodiment” or “in an embodiment” or the like in variousplaces throughout this specification are not necessarily all referringto the same embodiment.

Furthermore, the described features, structures, or characteristics maybe combined in any suitable manner in at least one embodiment. In thefollowing description, numerous specific details are provided to give athorough understanding of embodiments of the invention. One skilled inthe relevant art may well recognize, however, that embodiments of theinvention can be practiced without at least one of the specific detailsthereof, or can be practiced with other methods, components, materials,et cetera. In other instances, well-known structures, materials, oroperations are not shown or described in detail to avoid obscuringaspects of the invention.

The illustrated embodiments of the invention will be best understood byreference to the figures. The following description is intended only byway of example and simply illustrates certain selected exemplaryembodiments of the invention as claimed herein. It should be noted thatthe flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, apparatuses, methods and computer program products accordingto various embodiments of the invention. In this regard, each block inthe flowchart or block diagrams may represent a module, segment, orportion of code, which comprises at least one executable instruction forimplementing the specified logical function(s).

It should also be noted that, in some alternative implementations, thefunctions noted in the block may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustration, and combinations of blocks in the blockdiagrams and/or flowchart illustration, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts, or combinations of special purpose hardware and computerinstructions.

Specific reference will be made here below to FIGS. 1-3. It should beappreciated that the processes, arrangements and products broadlyillustrated therein can be carried out on, or in accordance with,essentially any suitable computer system or set of computer systems,which may, by way of an illustrative and non-restrictive example,include a system or server such as that indicated at 12′ in FIG. 3. Inaccordance with an example embodiment, all of the process steps,components and outputs discussed with respect to FIGS. 1-2 can beperformed or utilized by way of a processing unit or units and systemmemory such as those indicated, respectively, at 16′ and 28′ in FIG. 3,whether on a server computer, a client computer, a node computer in adistributed network, or any combination thereof.

Current learning techniques generally include a curriculum that is alinear list of topics and sub-topics. Thus, teaching the topic orlearning about the topic is generally linear, following the curriculum.For example, a teacher may include a syllabus that identifies the orderof how the topics will be taught during a class. Accordingly, when aperson uses resources to learn about a topic, it may be difficult toidentify which sub-topics actually need to be learned in order to learnthe desired topic. For example, if a person is attempting to learn onlya sub-concept within a book, it may be difficult for the person toidentify which sections of the book, or sub-topics, need to be learnedto learn only the desired sub-concept.

Additionally, many topics have many different techniques for acquiringthe desired knowledge. For example, the order in which differentsub-topics are learned for a target concept may be changed or modifieddepending on the instructor, author, student type, or the like. Theorder may make learning the desired topic harder or easier depending onthe base knowledge of the person learning the topic. For example, if aperson having a low base knowledge is attempting to learn a topic from abook that was written for a person having a higher base knowledge, theperson may have a very difficult time learning the concept. As anotherexample, one author may explain a topic in a manner which is easier tounderstand for one person and harder to understand for another person.

Additionally, many people learn at different levels and speeds. Forexample, one person may learn topics very quickly or may only requirehigh-level explanations of sub-concepts within the topic. On the otherhand, another person may learn very slowly or require in-depthexplanations of sub-concepts within the topic. Additionally, a persondoes not learn all topics at the same level. For example, a person mayneed only high-levels explanations of math topics, but may requirein-depth explanations of science topics. Additionally, if a person onlywants to learn about a particular sub-topic, the person may have to takean entire course that only briefly mentions that desired sub-topic.

Thus, one problem with the traditional linear learning techniques isthat they may be very inefficient or inadequate for a group of people.The linear learning technique is generally directed toward an averageperson who desires to learn the topic. However, if a person only wantsto learn a sub-concept included within a particular topic, it may bevery difficult for the person to identify which sub-concepts need to belearned and in which order to learn the desired sub-concept.Additionally, if the person does not learn at the same level as theaverage person, the person may have difficulty learning the topic.

Another problem with the traditional linear learning technique is thatit may be difficult for a person to identify which technique forlearning a topic will be most beneficial for that person. In otherwords, because many topics have different techniques for learning thetopic, one technique may be better suited for one learner while adifferent technique is better suited for a different learner. Forexample, if one learner has a higher base knowledge, learns at a fasterpace, and/or prefers to be challenged when learning topics, the learnermay desire a learning technique that reflects these attributes. If, onthe other hand, the learner has a lower base knowledge, learns at aslower pace, and/or prefers to have an easier learning experience, thelearner may desire a learning technique that reflects these attributes.However, using current linear techniques it may be difficult for theperson to identify which learning technique is reflected by the courseand/or course materials.

One solution relies on the teacher or author recognizing these problemsand providing relief to the student or reader. For example, the teacheror author may provide a form of a dependency graph or chart thatidentifies the relationship between one sub-concept and anothersub-concept when learning a target concept. However, these dependencygraphs merely identify pre-requisites between one sub-concept andanother sub-concept. The dependency graph does not identify arelationship between learning techniques, for example, if one learningtechnique is easier or harder than another learning technique, if onelearning technique has a faster pace than another learning technique,and the like. Additionally, even with the dependency graph, it may bedifficult for a person to identify which sub-concept would be necessaryto learn in order to learn a different sub-concept, rather than theentire target concept. Additionally, this technique does not provide amethod for identifying relationships between sub-concepts assub-concepts are added. For example, if a teacher includes a syllabushaving sub-concept dependency identifications, and, while teaching theclass, has to add a sub-concept, the relationship of this sub-concept tothe remaining sub-concepts is not identified.

Accordingly, the techniques and systems as described herein provide asystem and method for generating a syllabus for a student that is uniqueto the student and provides a learning sequence, including anidentification of learning modules, for learning a target concept. Thesystem may receive input identifying a goal of a student. The goal mayindicate not only a target or desired concept or topic, but may alsoidentify a desired expertise corresponding to the target concept. Inother words, the student may identify how proficient at, orknowledgeable about, the topic the student wants to be after engaging inthe learning. The student may also identify particular constraints, forexample, an amount of time that the student can spend, an amount ofeffort that the student wants to expend, an amount of money the studentis willing to spend, and the like.

The system may then generate a syllabus for the student. The syllabusmay identify a sequence of learning modules for the student to access inorder to learn the desired topic while meeting the constraints of thestudent. To identify this sequence, the system identifies relationshipsbetween not only sub-concepts of the target concept, but also betweenalternative sequences of sub-topics. For example, each target conceptmay include different sources and/or sub-topic sequences for learningthe target concept. The system identifies the relationships betweenthese sequences, for example, if one sub-concept source or order isharder or easier than another, if one requires a higher base knowledgethan another, and the like. Based upon the relationships betweenalternative sequences or series, the system can identify which sequencewill allow the student to learn the target concept at the desiredexpertise while still fulfilling the constraints of the student.

Such a system provides a technical improvement over current techniquesfor topic learning. The system provides a technique for identifyingrelationships between alternative sequences of sub-topics and/orlearning modules in order to identify a learning level for the sequence.Therefore, the system provides a technique for generating a syllabusthat is unique to a particular student or learner based upon theseidentified relationships. The student can identify a target concept,including target concepts that are sub-concepts of a larger targetconcept that may be the topic of one class. The syllabus is generatedbased upon the target concept and any additional information the studentmay provide, for example, how much time the student wants to spendlearning the topic, the desired expertise, and the like. Based uponidentified relationships between different learning techniques, thesystem identifies learning modules, which may include a combination oflearning sources, to be accessed by the student and the order of access,thereby providing a learning series to the student that is unique to thestudent and the known attributes of the student.

Additionally, using the same techniques, a syllabus can be generated foran entire classroom that identifies not only pre-requisites betweensub-concepts but also an identification of the relationships betweendifferent learning techniques, thereby allowing a student to identifythe learning method of the course and how difficult learning the topicwill be for the student. Thus, the systems and methods as describedherein provide a technique for allowing identification of not onlyrelationships between sub-concepts of a target concept, but alsoidentification of relationships between learning techniques for learningthe target concept. Accordingly, the system provides a techniqueallowing for a more efficient and effective learning experience by astudent.

FIG. 1 illustrates a method for generating a syllabus for a student thatis unique to the student and that provides a learning sequence,including identification of learning modules, thereby allowing thestudent to learn the target concept based upon attributes of thestudent. For ease of readability, the term student will be usedthroughout. However, it should be understood that the term studentrefers to any person that has identified a target concept for learning.In other words, the term student does not limit the person to only aperson who is enrolled in a classroom program. Additionally, the termstudent may refer to a group of students, for example, the syllabus maybe generated for or by a teacher for a group learning session or class.

At 101 the system may receive input identifying a goal of the student.The goal identifies the target concept to be learned by the student. Theinput to identify the target concept may be received using a variety ofmethods. For example, the student may select the target concept from alisting of target concepts. As another example, the student may type thename of the target concept, provide a link to an identified targetconcept, provide voice input, or the like. Additionally, the system mayidentify that the student appears to be searching for informationregarding a particular topic and may use this information to identify atarget concept. In other words, in this example, the system may useinformation not directly provided to the system and may providerecommendations for a target concept and a syllabus for learning therecommended target concept.

The goal may additionally identify a desired expertise corresponding tothe target concept. The desired expertise may be how proficient thestudent wants to be in the target concept after completing the learningsequence or syllabus. The desired expertise may be in different formats.For example, the student may provide an expertise indication of“proficient”, “expert”, or the like. As another example, the student mayprovide an expertise indication related to a particular grade or scorefor the concept, for example, “A-”, “C”, or the like. Even if thestudent is not actually graded in the learning series, the system maydetermine what sub-topics would need to be known in order to receivesuch a grade if the learning series was graded.

At 102 the system may receive input indicating constraints of thestudent for reaching the identified goal. The constraints may identifyhow much time the student is willing to expend in learning the concept,also referred to as a time budget. The time budget may identify how longthe student has to learn the target concept or is willing to spend tolearn the target concept. For example, the student may identify a timebudget of four weeks. This indicates that the student is willing tospend four weeks before reaching the identified goal. The constraintsmay also identify the level of effort the student is willing to expendto learn the topic, also referred to as an effort budget. The effortbudget may identify how many hours the student is willing to expend inlearning the topic. Using the example above, the student may identifythat the effort budget is ten hours over the identified time budget offour weeks. The effort budget may also be provided in smaller timeincrements, for example, the student may identify the effort budget istwo hours a day. In other words, the student is willing to dedicate twohours a day to learning the target concept over the four week timebudget. The effort budget may also identify particular days that thestudent is willing to use for learning, for example, the student may beable to dedicate time on Mondays, Wednesdays, and Fridays to learningthe target concept.

The student may also identify other constraints that may be used by thesystem. Another constraint may include a cost budget, for example, howmuch money the student is willing to expend to learn the target concept.Another constraint may include a travel budget, for example, if thestudent is willing to travel and how much. Another constraint mayinclude a location, for example, if the student is only willing toaccess a particular location for learning the target concept. Anotherconstraint may include a source constraint, for example, the student mayonly be willing to access online sources, classroom sources, companysources, or the like, to learn the target concept. In other words, theconstraints may identify what the student is willing to expend forlearning the target concept and how the student desires to learn thetarget concept.

The system may additionally identify a base knowledge of the student.This base knowledge may be identified with respect to the targetconcept. For example, if the target concept is “Spanish”, the system mayidentify the base knowledge of the student with respect to Spanish. Thebase knowledge may be categorized into different base knowledgecategories. Using the example of Spanish, the system may categorize thebase knowledge of the student into reading Spanish, speaking Spanish,and hearing Spanish. Thus, the student may have different baseknowledges for each of the categories. These different categories mayalso be aggregated to determine an overall base knowledge of thestudent. Identification of the base knowledge may be completed using avariety of techniques. For example, the student may identify a baseknowledge. As another example, the system may access sources thatidentify a base knowledge, for example, a transcript, grading report, orthe like. As another example, the system may provide a question/answersession that asks the student questions related to the base knowledge.As an example, the question/answer session may include questions askingthe student how comfortable he/she feels with respect to a particulartopic or sub-topic.

The system may then generate a syllabus for the student to reach theidentified goal while fulfilling the indicated constraints. The syllabusmay include a sequence of sub-concepts to be learned for reaching theidentified goal. The syllabus may also identify a source for learningeach sub-concept included in the syllabus. In other words, for eachsub-concept the syllabus may identify the lecture module that isrecommended for learning that sub-concept or a group of sub-concepts. Alecture module may include the specific source that is recommended forlearning the sub-concept. The source may include any source that can beaccessed by the student, for example, an Internet source, book chapter,book, class, company resource, or the like. The lecture module may alsohave an associated degree of hardness which identifies the level ofeffort that will be required to achieve the identified expertise. Inother words, the degree of hardness identifies what the level ofexpertise will be after taking the lecture module and how much effort isrequired for achieving that level of expertise.

To generate the syllabus the system may produce a plurality ofalternative sequences of sub-concepts at 103. The alternative sequencesmay include different sub-concepts for learning a topic. For example, ifa topic can be learned by learning alternate sub-topics a sequence orlearning series may be generated for each alternate sub-topic grouping.The alternative sequences or series may include sub-topics in adifferent order. For example, if a target concept can be learned bylearning the same sub-concepts in a different order, the system maygenerate a sequence or series for each of these alternative orders. Thealternative sequences may also include different lecture modules forlearning a sub-topic. For example, if a sub-concept can be learned usingdifferent sources, the system may generate a sequence or series for eachof these alternative sources. The alternative sequences each include acorresponding effort cost and a corresponding time cost. Eachalternative sequence may also identify other attributes associated withthe sequence or sub-concepts within the sequence, for example, a cost,location, source, and the like.

To produce the alternative sequences the system may first determine theconcept units or sub-concepts that are included in learning the targetconcept. The system may also determine the relationship between theconcept units. As an example, if the concepts are the Moon and Earth,the relationship between these concepts units is that the Moon revolvesaround the Earth. In other words, every topic consists of multiplesub-topics with each sub-topic discussing one thing that the student canunderstand to learn the topic. As stated before, learning a targetconcept may be accomplished using alternative sub-concepts. Therefore,some concept units may be included in one alternative sequence and notincluded in another alternative sequence. Once the concept units aredetermined the system may generate a knowledge graph corresponding tothe target concept and including the concept units. Each concept unitwould be represented as a node in the knowledge graph and therelationship between the concept units would be represented as an edgeconnecting two nodes. Once the knowledge graph is created for a targetconcept, the system may determine a sequence or series that can be usedto learn the target concept. In other words, from the knowledge graphthe system can identify one or more alternative sequences.

Once the alternative sequences have been identified, the system attemptsto determine which one or more alternative sequence will fulfill thegoal of the student, while staying within the identified constraints. Tomake this determination, the system identifies relationships betweensub-concepts of one alternative sequence and another alternativesequence. These relationships identify a “difficulty” difference of thesub-concept of one sequence with respect to a sub-concept of anothersequence. Some example relationships include “easier than”, “baseknowledge requirements”, “faster pace”, and the like. In other words,the relationship identifies a comparison of one or a group ofsub-concepts to one or a group of sub-concepts of another sequence orseries. To identify these relationships, the system may use a machinelearning approach that initially trains the system using a training setof information that allows the system to make predictions about new setsof information that are not specifically programmed into the system.

To identify the relationships the system may take, as input, historicalinformation from a set of current or previous students who are learningor have learned the target concept or sub-concepts of the targetconcept. The historical information may include student activityinformation related to the interaction of the student with the topic orsub-topic. For example, the system may identify the sequence that one ormore students used to learn the target concept. The system may thenidentify what grade each student received in the class directed toteaching the target concept. An overall higher average grade by onegroup of students may indicate that the sequence used by that group isan easier sequence than the sequence used by the group having the loweraverage grade. The system may also identify a base knowledge of thestudents when learning the sequence. For example, the system maydetermine that overall the group had a higher mathematical baseknowledge than another group.

The system may also access various other sources to identify otherinformation that may assist in determining the relationships. Forexample, the system may access a forum or other online location that canbe used by students. Using this information the system can determinewhat questions were asked by the students, which may assist inidentifying whether the student had difficulty with a particularsequence. The system may also determine if the student provided feedbackregarding a course or sequence of sub-concepts. This feedback mayprovide information related to how difficult the student found thecourse, whether the course moved too quickly, or the like.

The system can then build a classifier or ranking mechanism todiscriminate one relationship from another relationship. However,individually learning the relationships independently (i.e., comparing asingle sub-concept to another single sub-concept) may result in noisyand incorrect relationship labels. Specifically, it should be understoodthat a relationship between one sub-concept and another may be dependenton a different relationship between two other sub-concepts. Therefore,the system may use a joint learning of the relationships using a jointdistribution of edges between nodes. This joint distribution is basedupon generation of a directed acyclic graph (DAG) for the alternativesequences.

To generate the DAG, the system first identifies attributes of the nodesthat allow grouping of the edges conditionally independently from othergroups. For example, one attribute may be the source for the nodes. Asan example, if the nodes include sources Book A, Book B, Book C, andBook D, the system may group Book A and Book B in one group and Book Cand Book D in another group. Therefore, the edges between Books A and Bare independent from the edges between Books C and D. The system thencomputes each group of edges separately from other groups of edges. Oncethe edges are grouped, the system identifies prerequisites betweensub-concepts within the grouping. In other words, the system identifieswhich sub-concepts have to be learned before another sub-concept can belearned. The prerequisites may span across the grouping. Using the Bookattribute example, a prerequisite for a sub-concept from Book B may be asub-concept from Book A. The prerequisites may be identified based uponan order of occurrence from the source of the sub-concept, for example,a chapter listing in a book, a syllabus of a class, or otheridentification of dependency between one sub-concept and another. Theprerequisites then induce or create a DAG that includes sub-conceptsrepresented as nodes and relationships between the sub-conceptsrepresented as edges.

Once the DAG is generated, the system may topologically sort the set ofpossible relationship edges. In other words, the system may sort the DAGtopologically. The system may then identify and learn a form for theconditional distribution of the edge conditioned on the parents of theedge. The system may then factorize the joint probability of the edgesusing the topological order of the edges and the conditional probabilitydistribution, for example, using Bayes' theorem, also referred to as aBayesian framework, to learn each of the relationships edges. In otherwords, the system may use a logistic classifier to optimize thelikelihood of each relationship (e.g., the probability of the directionof each edge, etc.) and then choose the direction for the edge based onthe probability. The direction may be chosen based upon being thedirection having the highest probability of occurrence. For example, ifthe system is trying to determine which sub-concept is easier betweentwo sub-concepts, that relationship may be dependent on a relationshipbetween a previous set of sub-concepts, for example, if one of theprevious set of sub-concepts required a higher base knowledge. Thesystem can learn the joint distribution of these edges to determine therelationship both of these edges.

As an example, FIG. 2 illustrates an example DAG having identifiedrelationships between nodes 200. The system has grouped the sub-conceptsby source type, Book 1 201A and Book 2 201B. Thus, the system isattempting to identify relationships between the nodes of Book 1 201Aand the nodes of Book 2 201B. The system first identifies prerequisiterelationships 202, indicated by solid black lines, between the nodes. Ascan be seen, the prerequisite relationships can cross between thesub-groups, for example, Mass 203 from Book 1 201A is identified as aprerequisite for both Momentum 204 from Book 1 201A and Momentum 205from Book 2 201B. The system then identifies the relationships betweenother sub-concepts within the grouping to identify whether onesub-concept is “easier than” 206 another sub-concept, is a “faster pace”207 than another sub-concept, or requires a “higher math maturity” 208than another sub-concept.

The system then determines if one of the alternative sequences fulfillsthe desired goal (e.g., reaches the target concept at the desiredexpertise) and fulfills the indicated constraints at 104. If the systemdetermines that no alternative sequence reaches the goal and fulfillsthe constraints, the system may provide feedback to the user indicatingthat no sequence fulfilled the requirements at 106. The system may alsoprovide feedback of a sequence and indicate which requirement was notmet within that sequence. If, however, the system determines that one ormore of the sequences fulfills the requirement, the system may generatea syllabus based upon the sequence(s) that is identified as meeting therequirements at 105. Thus, the system produces a syllabus that is uniqueto a student and is based upon the student's needs, requirements, andgoals. As stated before, the syllabus may identify not only what orderthe sub-concepts should be learned in, but also what source should beused to learn the sub-concept. Thus, the syllabus may include acombination of sources for the identified alternative sequence.

As shown in FIG. 3, computer system/server 12′ in computing node 10′ isshown in the form of a general-purpose computing device. The componentsof computer system/server 12′ may include, but are not limited to, atleast one processor or processing unit 16′, a system memory 28′, and abus 18′ that couples various system components including system memory28′ to processor 16′. Bus 18′ represents at least one of any of severaltypes of bus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. By way of example, andnot limitation, such architectures include Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnects (PCI) bus.

Computer system/server 12′ typically includes a variety of computersystem readable media. Such media may be any available media that areaccessible by computer system/server 12′, and include both volatile andnon-volatile media, removable and non-removable media.

System memory 28′ can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30′ and/or cachememory 32′. Computer system/server 12′ may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34′ can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18′ by at least one datamedia interface. As will be further depicted and described below, memory28′ may include at least one program product having a set (e.g., atleast one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40′, having a set (at least one) of program modules 42′,may be stored in memory 28′ (by way of example, and not limitation), aswell as an operating system, at least one application program, otherprogram modules, and program data. Each of the operating systems, atleast one application program, other program modules, and program dataor some combination thereof, may include an implementation of anetworking environment. Program modules 42′ generally carry out thefunctions and/or methodologies of embodiments of the invention asdescribed herein.

Computer system/server 12′ may also communicate with at least oneexternal device 14′ such as a keyboard, a pointing device, a display24′, etc.; at least one device that enables a user to interact withcomputer system/server 12′; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 12′ to communicate withat least one other computing device. Such communication can occur viaI/O interfaces 22′. Still yet, computer system/server 12′ cancommunicate with at least one network such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20′. As depicted, network adapter 20′communicates with the other components of computer system/server 12′ viabus 18′. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12′. Examples include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

This disclosure has been presented for purposes of illustration anddescription but is not intended to be exhaustive or limiting. Manymodifications and variations will be apparent to those of ordinary skillin the art. The embodiments were chosen and described in order toexplain principles and practical application, and to enable others ofordinary skill in the art to understand the disclosure.

Although illustrative embodiments of the invention have been describedherein with reference to the accompanying drawings, it is to beunderstood that the embodiments of the invention are not limited tothose precise embodiments, and that various other changes andmodifications may be affected therein by one skilled in the art withoutdeparting from the scope or spirit of the disclosure.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions may also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A method, comprising: receiving input identifyinga goal of a student, wherein the goal indicates (i) a target concept tobe learned by the student and (ii) a desired expertise corresponding tothe target concept; receiving input indicating constraints of thestudent for reaching the identified goal, wherein the constraintscomprise (i) a time budget corresponding to a length of time the studentis willing to expend to learn the target concept and (ii) an effortbudget corresponding to a level of effort the student is willing toexpend to learn the target concept over the length of time; identifyinga base knowledge of the student with respect to the target concept,wherein the base knowledge comprises base knowledge categories eachhaving a base knowledge value; and generating a syllabus unique to andfor the student to reach the identified goal within the indicatedconstraints in view of the base knowledge of the student with respect tothe target concept, wherein the syllabus comprises a sequence ofsub-concepts to be learned for reaching the identified goal and alecture module recommended for learning each of the sub-concepts in thesequence, wherein each lecture module comprises a source for learning acorresponding sub-concept and a degree of hardness for the correspondingsub-concept, wherein the syllabus is generated by: producing a pluralityof alternative sequences of sub-concepts for reaching the identifiedgoal, each sequence of sub-concepts having both a determined,corresponding (i) effort cost and (ii) time cost, wherein determiningthe corresponding effort cost and time cost comprises identifyingrelationships between sub-concepts of one alternative sequence andsub-concepts of another alternative sequence, wherein the identifyingthe relationships comprises utilizing a machine learning system that istrained using a training set of information comprising historicalinformation from previous students who learned at least sub-concepts ofthe target concept and makes predictions about new relationships oncetrained, wherein the producing the plurality of alternative sequencescomprises determining concept units included in learning the targetconcept, determining a relationship between the concept units includedin learning the target concept, and generating a knowledge graphcomprising a plurality of nodes, each corresponding to one of theconcept units and edges, each connecting two of the plurality of nodesand representing a relationship between the connected two of theplurality of nodes; and determining, from the plurality of alternativesequences of sub-concepts, a particular one of the sequences ofsub-concepts that both reaches the target concept at the desiredexpertise and fulfills the indicated constraints.
 2. The method of claim1, wherein the generating a syllabus comprises identifying at least onesource for learning each sub-concept.
 3. The method of claim 1, whereinthe identifying relationships between sub-concepts is based uponhistorical information collected from previous students of thesub-concept.
 4. The method of claim 3, wherein the historicalinformation comprises queries submitted by the previous students.
 5. Themethod of claim 3, wherein the historical information comprises asequence of sub-concepts learned by one or more previous students tolearn the target concept.
 6. The method of claim 1, wherein the targetconcept and the sub-concepts of the target concept are aggregated into adirected acyclic graph based upon prerequisites between sub-conceptswithin a sequence of sub-concepts, wherein each node of the directedacyclic graph comprises one sub-concept and wherein each edge of thedirected acyclic graph identifies a relationship between two nodes. 7.The method of claim 6, wherein the identifying relationships comprisesconditionally grouping edges between sub-concepts of different sequencesindependently from other edge groupings.
 8. The method of claim 7,wherein the identifying relationships comprises learning the jointdistribution of the grouped edges within the directed acyclic graph. 9.The method of claim 8, wherein the identifying relationships comprisesfactorizing the joint distribution of the grouped edges.
 10. The methodof claim 9, wherein the identifying relationships comprises (i) using aclassifier to optimize the probability of the direction of each edgewithin the group and (ii) choosing, for each edge, the direction havingthe highest probability.
 11. An apparatus, comprising: at least oneprocessor; and a computer readable storage medium having computerreadable program code embodied therewith and executable by the at leastone processor, the computer readable program code comprising: computerreadable program code configured to receive input identifying a goal ofa student, wherein the goal indicates (i) a target concept to be learnedby the student and (ii) a desired expertise corresponding to the targetconcept; computer readable program code configured to receive inputindicating constraints of the student for reaching the identified goal,wherein the constraints comprise (i) a time budget corresponding to alength of time the student is willing to expend to learn the targetconcept and (ii) an effort budget corresponding to a level of effort thestudent is willing to expend to learn the target concept over the lengthof time; computer readable program code configured to identify a baseknowledge of the student with respect to the target concept, wherein thebase knowledge comprises base knowledge categories each having a baseknowledge value; and computer readable program code configured togenerate a syllabus unique to and for the student to reach theidentified goal within the indicated constraints in view of the baseknowledge of the student with respect to the target concept, wherein thesyllabus comprises a sequence of sub-concepts to be learned for reachingthe identified goal and a lecture module recommended for learning eachof the sub-concepts in the sequence, wherein each lecture modulecomprises a source for learning a corresponding sub-concept and a degreeof hardness for the corresponding sub-concept, wherein the syllabus isgenerated by: producing a plurality of alternative sequences ofsub-concepts for reaching the identified goal, each sequence ofsub-concepts having both a determined, corresponding (i) effort cost and(ii) time cost, wherein determining the corresponding effort cost andtime cost comprises identifying relationships between sub-concepts ofone alternative sequence and sub-concepts of another alternativesequence, wherein the identifying the relationships comprises utilizinga machine learning system that is trained using a training set ofinformation comprising historical information from previous students wholearned at least sub-concepts of the target concept and makespredictions about new relationships once trained, wherein the producingthe plurality of alternative sequences comprises determining conceptunits included in learning the target concept, determining arelationship between the concept units included in learning the targetconcept, and generating a knowledge graph comprising a plurality ofnodes, each corresponding to one of the concept units and edges, eachconnecting two of the plurality of nodes and representing a relationshipbetween the connected two of the plurality of nodes; and determining,from the plurality of alternative sequences of sub-concepts, aparticular one of the sequences of sub-concepts that both reaches thetarget concept at the desired expertise and fulfills the indicatedconstraints.
 12. A computer program product, comprising: a computerreadable storage medium having computer readable program code embodiedtherewith, the computer readable program code executable by a processorand comprising: computer readable program code configured to receiveinput indicating constraints of the student for reaching the identifiedgoal, wherein the constraints comprise (i) a time budget correspondingto a length of time the student is willing to expend to learn the targetconcept and (ii) an effort budget corresponding to a level of effort thestudent is willing to expend to learn the target concept over the lengthof time; computer readable program code configured to identify a baseknowledge of the student with respect to the target concept, wherein thebase knowledge comprises base knowledge categories each having a baseknowledge value; and computer readable program code configured togenerate a syllabus unique to and for the student to reach theidentified goal within the indicated constraints in view of the baseknowledge of the student with respect to the target concept, wherein thesyllabus comprises a sequence of sub-concepts to be learned for reachingthe identified goal and a lecture module recommended for learning eachof the sub-concepts in the sequence, wherein each lecture modulecomprises a source for learning a corresponding sub-concept and a degreeof hardness for the corresponding sub-concept, wherein the syllabus isgenerated by: producing a plurality of alternative sequences ofsub-concepts for reaching the identified goal, each sequence ofsub-concepts having both a determined, corresponding (i) effort cost and(ii) time cost, wherein determining the corresponding effort cost andtime cost comprises identifying relationships between sub-concepts ofone alternative sequence and sub-concepts of another alternativesequence, wherein the identifying the relationships comprises utilizinga machine learning system that is trained using a training set ofinformation comprising historical information from previous students wholearned at least sub-concepts of the target concept and makespredictions about new relationships once trained, wherein the producingthe plurality of alternative sequences comprises determining conceptunits included in learning the target concept, determining arelationship between the concept units included in learning the targetconcept, and generating a knowledge graph comprising a plurality ofnodes, each corresponding to one of the concept units and edges, eachconnecting two of the plurality of nodes and representing a relationshipbetween the connected two of the plurality of nodes; and determining,from the plurality of alternative sequences of sub-concepts, aparticular one of the sequences of sub-concepts that both reaches thetarget concept at the desired expertise and fulfills the indicatedconstraints.
 13. The computer program product of claim 12, wherein thegenerating a syllabus comprises identifying at least one source forlearning each sub-concept.
 14. The computer program product of claim 12,wherein the identifying relationships between sub-concepts is based uponhistorical information collected from previous students of thesub-concept.
 15. The computer program product of claim 12, wherein thetarget concept and the sub-concepts of the target concept are aggregatedinto a directed acyclic graph based upon prerequisites betweensub-concepts within a sequence of sub-concepts, wherein each node of thedirected acyclic graph comprises one sub-concept and wherein each edgeof the directed acyclic graph identifies a relationship between twonodes.
 16. The computer program product of claim 15, wherein theidentifying relationships comprises conditionally grouping edges betweensub-concepts of different sequences independently from other edgegroupings.
 17. The computer program product of claim 16, wherein theidentifying relationships comprises learning the joint distribution ofthe grouped edges within the directed acyclic graph.
 18. The computerprogram product of claim 17, wherein the identifying relationshipscomprises factorizing the joint distribution of the grouped edges. 19.The computer program product of claim 18, wherein the identifyingrelationships comprises (i) using a classifier to optimize theprobability of the direction of each edge within the group and (ii)choosing, for each edge, the direction having the highest probability.20. A method, comprising: identifying a target topic to be learned;identifying alternative sequences of sub-topics within the target topic,wherein each alternative sequence comprises a plurality of sub-topicsthat can be learned to learn the target topic and wherein eachalternative sequence comprises at least one sub-topic different from atleast one another alternative sequence, wherein the identifying thealternative sequences comprises determining concept units included inlearning the target topic, determining a relationship between theconcept units included in learning the target topic, and generating aknowledge graph comprising a plurality of nodes, each corresponding toone of the concept units and edges, each connecting two of the pluralityof nodes and representing a relationship between the connected two ofthe plurality of nodes; learning relationships between sub-topics of onealternative sequence and sub-topics of another alternative sequence,wherein the learned relationships indicate an effort difference betweenone sub-topic of one alternative sequence and a sub-topic of anotheralternative sequence, wherein the learning the relationships comprisesutilizing a machine learning system that is trained using a training setof information comprising historical information from previous studentswho learned at least sub-topics of the target topic and makespredictions about new relationships once trained; and generating, basedupon the learned relationships, a recommended course sequence that iswithin a predetermined effort level, wherein the recommended coursesequence comprises (i) one of the alternative sequences of sub-topics(ii) a learning source for learning each of the sub-topics, and (iii) adegree of hardness for learning each of the sub-topics.